input_data.py直接先将图片路径和标签对应为两个列表，然后用Tensorflow的模块生产批次batch
import os
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
train_path = 'D:/python学习/神经网络动物分类/train/'
test_path = 'D:/python学习/神经网络动物分类/test/'
classes = ["airplane", "automobile","bird","cat","deer",
"dog","frog","horse","ship","truck"]
def get_files(file_dir):
# file_dir: 文件夹路径
# return: 乱序后的图片和标签
img_list = []
label_list = []
for index, name in enumerate(classes):
class_path = file_dir + name + "/"
for img_name in os.listdir(class_path):
img_path = class_path + img_name
img_list.append(img_path)
label_list.append(int(index))
temp = np.array([img_list, label_list])
temp = temp.transpose() # 转置
np.random.shuffle(temp)
img_list = list(temp[:, 0])
label_list = list(temp[:, 1])
label_list = [int(i) for i in label_list]
return img_list, label_list
def get_batch(image, label, image_W, image_H, batch_size, capacity):
# image, label: 要生成batch的图像的地址和标签list
# image_W, image_H: 图片的宽高
# batch_size: 每个batch有多少张图片
# capacity: 队列容量
# return: 图像和标签的batch
# 将python.list类型转换成tf能够识别的格式
image = tf.cast(image, tf.string)
label = tf.cast(label, tf.int32)
# 生成队列
input_queue = tf.train.slice_input_producer([image, label])
image_contents = tf.read_file(input_queue[0])
label = input_queue[1]
image = tf.image.decode_jpeg(image_contents, channels=3)
image = tf.image.resize_images(image, [image_H, image_W], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
image = tf.cast(image, tf.float32)
image_batch, label_batch = tf.train.batch([image, label],
batch_size=batch_size,
num_threads=64, # 线程
capacity=capacity)
return image_batch, label_batch
# 测试两个函数是否成功运行
"""
if __name__ == '__main__':
BATCH_SIZE = 2
CAPACITY = 256
IMG_W = 32
IMG_H = 32
image_list, label_list = get_files(train_path)
image_batch, label_batch = get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
with tf.Session() as sess:
i = 0
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop() and i < 1:
img, label = sess.run([image_batch, label_batch])
for j in np.arange(BATCH_SIZE):
print("label: %d" % label[j])
plt.imshow(img[j, :, :, :])
plt.show()
i += 1
except tf.errors.OutOfRangeError:
print("done!")
finally:
coord.request_stop()
coord.join(threads)
"""
model.py函数实现了模型以及预测
#coding=utf-8
import tensorflow as tf
def inference(images, batch_size, n_classes):
with tf.variable_scope('conv1') as scope:
# 卷积盒的为 3*3 的卷积盒，图片厚度是3，输出是16个featuremap
weights = tf.get_variable('weights',
shape=[3, 3, 3, 16],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[16],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(pre_activation, name=scope.name)
with tf.variable_scope('pooling1_lrn') as scope:
pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')
norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
with tf.variable_scope('conv2') as scope:
weights = tf.get_variable('weights',
shape=[3, 3, 16, 16],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[16],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
pre_activation = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(pre_activation, name='conv2')
# pool2 and norm2
with tf.variable_scope('pooling2_lrn') as scope:
norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')
with tf.variable_scope('local3') as scope:
reshape = tf.reshape(pool2, shape=[batch_size, -1])
dim = reshape.get_shape()[1].value
weights = tf.get_variable('weights',
shape=[dim, 128],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[128],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# local4
with tf.variable_scope('local4') as scope:
weights = tf.get_variable('weights',
shape=[128, 128],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[128],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')
# softmax
with tf.variable_scope('softmax_linear') as scope:
weights = tf.get_variable('softmax_linear',
shape=[128, n_classes],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[n_classes],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')
return softmax_linear
def losses(logits, labels):
with tf.variable_scope('loss') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
(logits=logits, labels=labels, name='xentropy_per_example')
loss = tf.reduce_mean(cross_entropy, name='loss')
tf.summary.scalar(scope.name + '/loss', loss)
return loss
def trainning(loss, learning_rate):
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = optimizer.minimize(loss, global_step= global_step)
return train_op
def evaluation(logits, labels):
with tf.variable_scope('accuracy') as scope:
correct = tf.nn.in_top_k(logits, labels, 1)
correct = tf.cast(correct, tf.float16)
accuracy = tf.reduce_mean(correct)
tf.summary.scalar(scope.name + '/accuracy', accuracy)
return accuracy
train.py函数实现了训练过程
import os
import numpy as np
import tensorflow as tf
import input_data
import model
N_CLASSES = 10
IMG_H = 32
IMG_W = 32
BATCH_SIZE = 200
CAPACITY = 2000
MAX_STEP = 15000
learning_rate = 0.0001
def run_training():
train_dir = "D:\\python学习\\神经网络动物分类\\train\\"
logs_train_dir = "logs\\"
train, train_label = input_data.get_files(train_dir)
train_batch, train_label_batch = input_data.get_batch(train,
train_label,
IMG_W,
IMG_H,
BATCH_SIZE,
CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
sess = tf.Session()
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
if step % 100 == 0:
print("Step %d, train loss = %.2f, train accuracy = %.2f%%" % (step, tra_loss, tra_acc))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, "model.ckpt")
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print("Done training -- epoch limit reached.")
finally:
coord.request_stop()
coord.join(threads)
sess.close()
if __name__ == '__main__':
run_training()